1. Field of the Invention
The present invention relates to a method, apparatus, and program for detecting a contour of a particular region in an image. More specifically, the present invention relates to an image processing method, apparatus, and program for detecting a contour of a tumor area in a medical image.
2. Description of the Related Art
In the medical field, computer aided diagnosis (CAD), in which an image analysis is performed on a digital medical image (hereinafter, simply referred to as “medical image”) for diagnosis, is used in order to improve diagnostic accuracy and to reduce the burden on the medical staff, including doctors, who performs radiological image reading.
One of the known CAD systems, for example, automatically analyzes a malignancy grade of a tumor based on radiological information, such as the size of a lesion in a medical image. U.S. Pat. No. 6,738,499 (Patent Document 1) proposes a method in which an approximate contour of a tumor is manually set by a radiological doctor, and the likelihood of malignancy of the tumor is automatically analyzed by an artificial neural network (ANN) method based on radiological characteristic amounts of the tumor, such as, for example, the size of the tumor, distribution of CT values inside of the tumor, and the like, obtained by the contour.
For performing medical diagnosis based on such radiological information of a tumor area, it is necessary to accurately detect a tumor area or the contour thereof in a chest CT image. As one of the methods for detecting tumor areas, the following method is known as described, for example, in Japanese Unexamined Patent Publication No. 2003-250794 (Patent Document 2). That is, the method includes the steps of: extracting vessel candidate regions in a plurality of sequential CT images through binarization process; selecting a region with a circumscribed rectangular solid having a greater volume from the vessel candidate regions as the vessel region; performing an erosion process and selecting isolated regions on the vessel region as tumor candidate regions; and detecting a tumor candidate region having a substantially spherical shape as a tumor area.
Further, a non-patent literature, “Robust Anisotropic Gaussian Fitting for Volumetric Characterization of Pulmonary Nodules in Multislice CT” by K. Okada et al., IEEE Transactions on Medical Imaging, Vol. 24, No. 3, pp. 409-423, 2005, (Non-patent Document 1) proposes a method in which the intensity distribution of a tumor area is assumed to be Gaussian distribution, and the average and variance of an anisotropic Gaussian distribution corresponding the most to the intensity distribution of the tumor area are calculated, and an elliptical contour centered on the position of the median value of the Gaussian distribution is obtained.
Still further, U.S. Pat. No. 6,973,212 (Patent Document 3) discloses a method in which a target region in an image specified by a user is extracted using likelihood that each pixel is a pixel representing the target area or background area calculated based on information regarding a particular pixel representing the target region and a particular pixel representing the background area, and likelihood that adjacent pixels are pixels of the same region calculated based on local contrast of the image (edge information), and the contour of the extracted target region is obtained.
Further, a non-patent literature, “A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography” by S. Timp and N. Karssemeijer, Med. Phys. Vol. 31, No. 5, 2004 (Non-patent Document 2) describes a method in which an evaluation value, which indicates whether or not each pixel in an image is a pixel representing the contour of a tumor area, is obtained using local contrast in the image (edge information), expected size of the tumor area, and intensity distribution of the image, and an optimum contour path obtained through dynamic programming based on the obtained evaluation value is determined as the contour of the tumor area.
In liver cancer diagnosis using CT images, however, the contrast between a tumor and background liver tissue is smaller in comparison with image diagnosis for lung cancer and breast cancer, and tumors have variety of shapes. Thus, the method proposed in Patent Document 2, in which a tumor area is determined from the regions extracted through binarization process, may not correctly detect tumor areas in many occasions. FIGS. 2A to 2D and FIGS. 3A to 3D illustrate one-dimensional intensity profiles on straight lines passing through liver tumors. Here, “A” and “A′” denote a contour portion of a tumor. For example, in FIG. 2B representing a one-dimensional intensity profile on the straight line A-A′ of FIG. 2A, the contrast between the tumor and background liver tissue is great, so that the tumor area may be detected through binarization process using an appropriate threshold value. However, in FIGS. 2D, 3B, and 3D, the distinction between the inside and outside of the tumor may not be made only by the intensity, so that detection of the tumor area through binarization process is difficult.
The method for obtaining a contour of a target region based on the local contrast (edge information) of an image disclosed in Patent Document 3 or Non-patent Document 2 has a problem that the contour extraction is susceptible to changes in local contrast, and as the contrast between the target region and background area becomes small, the contour detection capability is degraded.
Further, the method proposed in Non-patent Document 1, which performs contour detection on the assumption that the intensity distribution of a tumor follows Gaussian distribution, may not be applied to the case in which the inside of the tumor has a lower intensity value, with low intensity variation, than the background tissue as illustrated in FIG. 3B, and the case in which the intensity distribution may not be approximated by Gaussian distribution due to, for example, the presence of a plurality of Gaussian intensity distributions inside and outside of the tumor as illustrated in FIG. 3D, and the like. Further, the contour is obtained as an ellipse, so that an accurate contour may not be obtained for a tumor having an irregular shape, thereby the accuracy of the image diagnosis may be degraded.